Science topic

Logistics - Science topic

Logistics is the management of the flow of resources, between the point of origin and the point of destination in order to meet the requirements of customers or corporations.
Questions related to Logistics
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Hello, fellow researchers and professionals!
I am a logistics and supply chain management student, eager to deepen my technical knowledge and practical skills in the field. I'm particularly interested in hands-on projects that address real-world challenges, such as optimizing last-mile delivery, improving warehouse efficiency, or leveraging data analysis for better inventory forecasting.
I would greatly appreciate your insights, ideas, or recommendations for project topics that could help me enhance my understanding of logistics and supply chain processes. Suggestions involving tools like Python, Excel, simulation software, or even case studies are highly welcome!
Thank you in advance for your valuable contributions. I look forward to learning from your expertise!
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Hallo Annas
as my experience as a professional in supply chain especially in focus on inventory management, the first think you need to know and learn is flow process of supply chain it self. Just pick one topic, maybe fertilizer or sparpart material, from first mile until last mile,
1. Procurement Aspect > kind of Letter of Creedits etc
2. Shipping > Incoterms , BL
3. Customs
4. Inland Transportation > Truck, 40 feets or 20 feets,..
5. Warehousing > Inbound , handling , outbound
Sincerely
Faishal
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Hello everyone, I worked around 5years in Domestic Purchase and Inter-company Logistics,
My Idea is making Procurement in smarter way with systematic knowledge directly interconnected with computer softwares Globally like a chain to reduce human interaction like physical counts and monitoring the stocks to reorder the Items with the help of IOT interconnecting all departments in serial and dynamic model to provide information to the managers to make just a decision with received info like order qty xxx -> Existing stock xxx -> Automatic lean Material plan comparing with other items -> Exact needed Qty xxxx to order.
( Incoming Job Order ---> Lean Production Plan with,
--->Incoming RM Stocks Counts & Qty in Store or warehouse and also in unused Vendor & Supplier consignment stock,
---> Lean Production ---> Removed Qty Stock after Production and waste
---> Dispatch & Shipping ---> Transits Logistics ---> Coordination with Supplier and Vendors )
I'm not aware if this technology is already in use. This is just a thought draft, can this be done for Master Thesis?
Please share your advice a view over this.
Thanks in Advance!!
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Evaluating the Influence of Packaging on Consumer's Respect Purchase
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You can suggest from Logistic and supply chain management Or Sustainablity management
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Welding of Gen 3 steels
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This is the 100th article read and commented
Dear Ahmad Fairuz, Liane Okdinawati, and Adirizal Niza
I have read your paper
AHP Application to Select Logistical Location in Upstream Oil and Gas Operation: A Case Study
My comments
1.- In the abstract you refer to three criteria, when in reality you are considering five. However, in my opinion, these are not near enough criteria to use in a project of this magnitude.
For instance, I think that you need to consider also distances, and in risk, many different cases like personal, equipment failure, fatalities, damages by storms, potential blackout because failures of electrical generating equipment on board of platform. What about emergencies and potential evacuations, shortage of spare parts, for pumps and drilling, etc.?
Even when you present a very good collection of valuable opinions from experts regarding criteria, I believe that they are incomplete.
Do you have contingency and remediation plan?
2- In page 1 you say “To reduce the complexity, the Analytical Hierarchy Process (AHP) is used in this study as part of Multi-Criteria Decision Making (MCDM) that can break down the problem into a hierarchy and provide the weight of each element which will result in one best option as a business solution”
You can break a problem to study it in more detail, but not to solve it, because normally, all aspects of the problem are related. For instance, you can have a very ill person on the platform who needs to transported to a hospital, and you need to coordinate many things that link these two issues, the ill guy, the transportation to a hospital and the treatment.
You can plan and study the health problem on a platform and take all the necessary measures, and also analyze independently the general transportation problem, but it is evident that the health problem on the platform may be related with transportation. Consequently, you cannot make a pair-wise comparison between health and transportation criteria and determine which is more important, let alone, in what degree, because health has a component of transportation, and both are complementary. How do you evaluate the mutual contribution?
Because of this, this scenario can be solved by MCDM method, but not using AHP or using the weights its generate.
You agree with this when you say “This will involve the use of specialized transport equipment and material storage in strategic locations, as well as the coordination of complex supply chains involving multiple related stakeholders”.
Therefore, why do you use a MCDM method that does not consider dependency between criteria, but the opposite? It is not an AHP fault, because its structure if for other kind of problems, but it is on the people that use it incorrectly, which, in my opinion, is your case.
Chang et al, that you mention, are by far more realistic that the others experts that are happy with only 4 or 5 criteria
3- In page 3 you say “AHP is one of the MCDM methods found by Thomas Saaty, which is useful for organizing complex problem”
This is incorrect. AHP is unable to deal with complex problems because its hierarchical lineal structure, that is not followed by real-life problems. Today problems have many elements interlinked, in some sort of a network, instead of a lineal vertical relationship.
4- In page 3 “AHP breaks down the problem into a hierarchy, which helps simplify its complexity and displays the connections between various objectives (or criteria) and potential alternatives”
Not really. By breaking a problem, you certainly can better understand it and simplify your comprehension, but the problem is not altered by that. It remains the same.
Just take a car as example; you can break it is engine, body, transmission, etc. to study each separately, but you cannot improve the engine if you do not consider the other parts that are intimately related to it, like power transmission needed to the wheels, type of tires, body weight, body aerodynamics, electrical system, etc. This is a system, and you cannot break it, the same in MCDM problems, they are normally systems, and must be solved as a whole.
5- The AHP method arbitrarily estimates the trade-offs of criteria or objectives, and assumes (Saaty words), that they are equivalent to weights. In addition, since these weights were conceived WITHOUT considering the alternatives, they do not play any role in selecting alternatives. This is not only my opinion but comes from the Shannon Theorem
6- In page 3 “judgment is consistent and does not cause bias in the result”
You forgot to say that if is not consistent, it is forced to be, using a formula.
In addition, even if there is consistency, why is it assumed that what is in the mind of as person is applied to reality?
I hope these comments may help you
Nolberto Munier
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Dear Christina Samiotakis
Thank you very much for your question
What I tried to say, is that most scenarios or problems in MCDM with a set of alternatives subject to the compliance of a set of criteria, cannot be studied in isolation, and I put the example of cars.
If you know about cars and must design a new model, perhaps you commence with the body aerodynamics, and start thinking about the engine, for which you think that your car must have a 250 HP engine. Then you test the engine and in the engine test bank you get 256 HP. Fantastic!
Fine, then you start thinking in the electronics, after on thesuspension, followed by tires, etc. You assemble all the elements and to your dismay find that the engine 250 HP cannot reach that power in operation, even when each of them has been optimized.
This happens because you designed each component separately forgetting that everything is related, especially the engine. If you observe the speed, you did not realize that it also depends of the aerodynamics of the body and that speed increase with a cubic relationship with the engine. In reality, what you should have done is compute all components simultaneously in such a way that the engine knows how power it needs and adjust to itIn other words, the car is a system, and as that should be treated
The same happens in our world; you cannot speak of environment if it is not related to cost, emissions, health, etc. Remember that environment is the intersection, not the summation,of components. In other words, you need an environment that complies with nature, health and economics simultaneously
The best example of a system is our body, the most perfect machine we cannot even dream in building. If you go to the doctor because whatever symptoms you have, the doctor of course will give you a medication for that, but he /she will also take your blood pressure, your food habits etc., Because everything is related.
The same in MCDM, and what I criticize the most is that almost 99% of the methods considers coponents independently. Thsat is, the sum instead of multiplying
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Dear Researchers,
Can you explain how Chatbots for logistics can really transform the supply Chain industry and any known real world use cases are in applicability?
Thanks.
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Chatbots transform logistics by automating customer service, streamlining order management, and enhancing inventory control. They handle tasks like tracking shipments, managing orders, and providing real-time inventory updates, which improves efficiency and reduces human intervention. Integrated with AI, chatbots can also predict delays, optimize routing, and forecast demand, enhancing decision-making.
Real-world examples include DHL's chatbot for tracking and scheduling, UPS's chatbot for customer inquiries and shipping estimates, and Amazon's Alexa for warehouse management. These applications improve response times, reduce costs, and offer greater transparency, significantly optimizing supply chain operations.
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MUSE headbands employ four electrodes and read EEG brainwaves. I am wondering if the devices are logistically feasible for field research, and what challenges researchers have encountered during data collection.
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Stephen Shannon Muse is probably not a perfect product, but it is certainly cheap and easy to handle. Obviously, it cannot be considered as effective as a professional medical EEG, but it provides very interesting basic information. I use it with sportsmen, and for them I establish cognitive abilities before sporting events. It's all at an experimental level, so maybe in a year's time there will be something better (that's how technology works). You can also use it during competitions because you just wear a lycra band if it's Muse2, or MuseS is already an elastic band. There are many softwares you can use with Muse, one of which my team created especially for it: it provides real-time data or you can download the data in csv (it is a commercial product). The software produces insights created specifically for the sporting environment. There are alternatives to Muse: they are all more expensive. But they should be tried. If you need more info write to me at rosso.gianluca@gmail.com
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🚀 Exciting News! As a supply chain specialist who has contributed to academia and various industries since 2014, I’ve launched a YouTube channel called Supply Chain and Logistics Mastery! 🎥 🎉
By engaging with all of you bright-minded juniors, specialists, managers, and researchers, we can gain invaluable insights and contribute to the growth of knowledge and best practices in the fascinating world of supply chain management and logistics. 📈
Participants: Beginners to managers, and researchers
Fundamental Topics: Inventory Management, Demand Forecasting, Material Requirements Planning (MRP), Production Planning, Warehousing, Distribution, and Last-Mile Delivery!
Advanced Topics: Supply Chain 4.0, Disruptive Technologies-Enabled Supply Chain Innovation, Supply Chain Decentralization, Circular Supply Chain, Green Supply Chain, and more!
Training Structure: Step-by-step courses that explore the ins and outs of topics—from simple definitions to mastery levels. Each training video is just around 3 minutes long perfect for fitting into our busy schedules!
Join me on this journey of growth and learning! 🌟
Channel Handle (google it): @SCLmastery
Together, let’s strive for excellence! 💪 I look forward to seeing you there!
Feel free to share any thoughts or add any additional details that could enhance the community!
Best, Keyvan
#SupplyChain #Logistics #ProdutionPlanning #productionScheduling #InventoryManagement #InventoryControl #Distribution #Warehousing #LastmileDelivery # MaterialRequirementPlanning #MRP #CircularSupplyChain #GreenSupplyChain #SupplyChainInnovation #SupplyChain4.0 #DisruptiveTechnology #SupplyChainDecentralization #ProfessionalDevelopment #CareerGrowth #CollaborativeLearning #KnowledgeSharing
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Please note that I have written three textbooks on supply chain excellence. They are: 'Supply Chain Leadership', 'Supply Chain Processes', and 'Supply Chain Analytics'.
Taken together, they provide a roadmap to supply chain competence leading to genuine competitive advantage. Hope they can assist your cause.
Cheers,
Peter Robertson.
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🚀 Exciting News! As a supply chain specialist who has contributed to academia and various industries since 2014, I’ve launched a YouTube channel called Supply Chain and Logistics Mastery! 🎥 🎉
By engaging with all of you bright-minded juniors, specialists, and managers, we can gain invaluable insights and contribute to the growth of knowledge and best practices in the fascinating world of supply chain management and logistics. 📈
Participants: Beginners to managers
Fundamental Topics: Inventory Management, Demand Forecasting, Material Requirements Planning (MRP), Production Planning, Warehousing, Distribution, and Last-Mile Delivery!
Advanced Topics: Supply Chain 4.0, Disruptive Technologies-Enabled Supply Chain Innovation, Supply Chain Decentralization, Circular Supply Chain, Green Supply Chain, and more!
Training Structure: Step-by-step courses that explore the ins and outs of topics—from simple definitions to mastery levels. Each training video is just around 3 minutes long perfect for fitting into our busy schedules!
Join me on this journey of growth and learning! 🌟
Channel Handle (google it): @SCLmastery
Together, let’s strive for excellence! 💪 I look forward to seeing you there!
Feel free to share any thoughts or add any additional details that could enhance the community!
Best, Keyvan
#SupplyChain #Logistics #ProdutionPlanning #productionScheduling #InventoryManagement #InventoryControl #Distribution #Warehousing #LastmileDelivery # MaterialRequirementPlanning #MRP #CircularSupplyChain #GreenSupplyChain #SupplyChainInnovation #SupplyChain4.0 #DisruptiveTechnology #SupplyChainDecentralization #ProfessionalDevelopment #CareerGrowth #CollaborativeLearning #KnowledgeSharing
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Supply Chain is the process that starts from the production of a product to its delivery to the customer.
Logistics Specialist is a profession widely used in supply chain management, warehousing, transportation and distribution parts. This user, who has knowledge about the planning, coordination and management of the logistics sector, works towards the goal of customer satisfaction and cost savings.
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Hi everyone,
Hope you are all in good well and health.
I am currently pursuing my degree in Bachelor of Science in Logistics. I am searching for reliable survey questions that I can use in my thesis project titled "The Influence of Implementing Artificial Intelligence on Businesses in the Logistics Industry and Its Effect on Workforce Displacement."
Could you please guide how I can or where can I find the questions?
Thank you for your time and assistance.
Best regards,
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Hello Anfal,
These questions may be useful for your research on AI & Logistics.
Ethical and Regulatory Concerns:
What ethical concerns do you associate with the use of AI in logistics?
Do you think regulations should be implemented to manage the impact of AI on workforce displacement in the logistics sector?
Should companies that develop AI technologies be responsible for addressing workforce displacement issues caused by their products?
Preparing for AI and Workforce Adaptation:
What measures should companies take to mitigate the negative impact of AI on job displacement?
Do you believe current educational programs are adequate to prepare the workforce for AI integration in logistics?
What kind of training do you think logistics professionals will need to work effectively alongside AI technologies?
Follow my research papers on logistics, E-commerce, RPA an ERP solutions etc., if you find interesting, Thank you!
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I am optimizing a multi-stage optimization model for disaster response relief delivery. In this regard, I want to know the realistic values of cost parameters related to the operating and setup costs of temporary relief logistics facilities.
Can anyone guide me what are the realistic ranges of these costs, and how they are related to each other, and with the capacity of temporary relief facilities?
Also, please comment on the effect of the planning horizon's length on these costs.
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1. Cost Parameters for Temporary Relief Logistics Facilities
A. Setup Costs:
  • Construction and Installation: These include the costs of setting up temporary facilities such as tents, mobile units, or modular buildings. Costs can vary significantly based on the type and scale of the facility. Typical Ranges: Setup costs can range from a few thousand dollars for simple tents to several million dollars for larger modular units with full amenities. Factors Influencing Costs: Location, infrastructure requirements (e.g., utilities, access roads), and the facility's scale.
B. Operating Costs:
  • Maintenance and Utilities: These cover expenses for maintaining the facility and providing utilities such as electricity, water, and sanitation.Typical Ranges: Operating costs might range from $500 to $5,000 per day, depending on facility size and complexity. Factors Influencing Costs: Duration of use, local utility rates, and facility size.
  • Staffing and Supplies: Costs related to personnel (e.g., security, administrative staff) and supplies (e.g., medical equipment, food).Typical Ranges: Staffing costs can vary widely based on the number and type of staff. Supplies costs depend on the nature of relief activities.
2. Relationship Between Costs and Facility Capacity
  • Scaling Costs: As the temporary relief facility's capacity increases, both setup and operating costs generally increase, but not necessarily in a linear fashion. Larger facilities may benefit from economies of scale, where the cost per unit decreases as the size increases. For example, a larger facility might have a higher initial setup cost, but the per-person cost of providing services may decrease.
  • Capacity Planning: To optimize costs, it's essential to match the facility’s capacity with anticipated demand. Underestimating capacity can lead to higher per-unit costs due to inefficiencies and overcrowding while overestimating can lead to higher fixed costs.
3. Effect of Planning Horizon’s Length on Costs
  • Short-Term vs. Long-Term Costs: The length of the planning horizon affects both setup and operating costs. Short-Term Planning: Short-term facilities might have higher per-day costs due to the temporary nature of the setup and possible lack of economies of scale. Long-Term Planning: Longer-term facilities might benefit from more efficient resource use and lower per-day costs, but they also incur additional costs related to long-term maintenance and staffing.
  • Cost Trade-offs: Longer planning horizons might allow for more permanent or semi-permanent solutions that reduce daily operating costs but increase upfront setup costs. Balancing these trade-offs is crucial for cost-effective planning.
4. Practical Recommendations
  • Benchmarking: Review case studies and reports from similar disaster response scenarios to understand realistic cost ranges. Organizations like the World Health Organization (WHO), the United Nations (UN), and various NGOs often publish cost data related to disaster response facilities.
  • Cost Modeling: Use cost models that account for both fixed and variable costs and incorporate scenarios for different facility sizes and durations. Tools like Excel or specialized optimization software can help develop these models.
  • Consult Experts: Engage with experts in disaster response logistics and facility management to get accurate cost estimates and insights into cost management strategies.
Summary
  • Setup Costs: Typically range from thousands to millions of dollars, depending on facility type and scale.
  • Operating Costs vary from $500 to $5,000 per day, influenced by facility size and local conditions.
  • Capacity Relationship: Larger facilities may have higher setup costs but lower per-unit operating costs.
  • Planning Horizon: Longer horizons might reduce per-day costs but increase initial setup costs. Your model should consider both short-term and long-term implications.
Understanding these cost parameters and their relationships will help you optimize your disaster response logistics model effectively.
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RBV and SBV
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Hello dears.
In my research thesis I need use a holistic view method such as 2 dimensions phase space , 3 dimensions phase space or maps such chirikov map , logistics map and... With matching deep Neural networks for Exploring Brain Dynamic via EEG in music composition.
Please guide me about some things.
1.which features better I extraction? & How?
2.which neural networks you prefer?
3.this is a good topic for thesis?
Thank you so much for help and support.
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i am not aware
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agricultural economics
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Supply chain logistics play a critical role in reducing post-harvest losses and increasing agricultural efficiency by optimizing the movement of agricultural products from farms to consumers. Efficient logistics ensure timely and proper handling of harvested crops, minimizing exposure to spoilage, pests, and physical damage. This includes appropriate storage facilities, transportation methods, and packaging techniques tailored to the specific needs of different crops. Additionally, logistics help in maintaining the quality and freshness of agricultural products during distribution, thereby reducing waste and improving market access for farmers. By streamlining supply chains, implementing cold chain systems where necessary, and enhancing coordination between stakeholders, logistics contribute significantly to sustainable agriculture practices and food security, ensuring that more of the harvest reaches consumers in optimal condition while reducing environmental impact.
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Hello all,
I am currently finishing my Masters Degree of Management and Sustainability and I am aiming to do a PhD in January. However, my mind is blank to think of a good research question as i have just finished my second semester with 3 assignments worth 15,000 words in total and, now starting on another assignment.
I was wondering if anyone can advise me or help me come up with a research questions based on my interests?
I am interested in doing a PhD on Business Ethics and Sustainability or purely on Sustainability topics.
At University, I finished these units which were my favourite:
- Critical Enquiry for Entrepreneurs and Leaders (I write a journal article on how Neoliberalism influence the sustainable practices of the fast fashion industry).
- Social, Ethical and Environmental Enterprise for Leaders
- Business Ethics, Sustainability and Responsible Leadership
- Logistics and Supply Chain Practices
- Sustainability and Sustainable Development
Any comments would be much appreciated.
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Generally, it would be optimal to pick a topic based on the area of expertise of your prospective doctoral supervisor. I would contact whomever you plan on working with and ask them about their research program (e.g., grant-funded projects) and topic within that that you could work on.
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How does generative artificial intelligence technology combined with Big Data Analytics and other Industry 4.0 technologies help in planning and improving production logistics management processes in business entities, companies and enterprises?
Production logistics management in a manufacturing company is currently one of the key areas of business management that significantly affects the level of technical and organizational efficiency of business operations. The change in the level of technical and organizational efficiency of business operations also usually has a significant impact and correlates with the issue of business efficiency and affects the financial results generated in the business entity. Among the key segments of logistics in the enterprise are also internal production logistics, on the way of organization of which the efficiency of the operation of production processes and the efficiency of the enterprise also largely depends. In recent years, more and more companies and enterprises have been optimizing production logistics through the implementation of information systems and automation of individual operations in the process. Production logistics is mainly concerned with ensuring the optimal flow of materials and information in the process of producing all types of goods. Production logistics does not deal with the technology of production processes, but only with the organization of the production system together with the storage and transport environment. Production logistics is mainly concerned with the optimization of all operations related to the production process, such as: supplying the plant with raw materials, semi-finished products and components necessary for production; transporting items between successive stages of production; and transferring the finished product to disposal warehouses. Precisely defining optimal production logistics is a lengthy process, requiring analysis and modification of almost every process taking place in a company. One of the key factors in the optimization of production logistics is the reduction of inventory levels and their adjustment to the ongoing production process. This translates directly into a decrease in storage costs. Effective management of production logistics should ensure timely delivery, while maintaining high product quality. Effective production logistics management can be supported by the implementation of new Industry 4.0/5.0 technologies, including Big Data and generative artificial intelligence.
The key issues of opportunities and threats to the development of artificial intelligence technology are described in my article below:
OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT
In view of the above, I address the following question to the esteemed community of scientists and researchers:
How does the technology of generative artificial intelligence, combined with Big Data Analytics and other Industry 4.0 technologies, help to plan and improve production logistics management processes in business entities, companies and enterprises?
How does generative artificial intelligence technology help in planning and improving production logistics processes in an enterprise?
What do you think about this topic?
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best wishes,
Dariusz Prokopowicz
The above text is entirely my own work written by me on the basis of my research.
In writing this text, I did not use other sources or automatic text generation systems.
Copyright by Dariusz Prokopowicz
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Dariusz Prokopowicz Generative artificial intelligence (AI) technology, when combined with Big Data Analytics and other Industry 4.0 technologies, can significantly enhance planning and improve production logistics management processes in business entities, companies, and enterprises. Here's how these technologies work together to achieve these goals:
  1. Predictive Analytics and Forecasting: Generative AI algorithms can analyze large volumes of historical production and logistics data to identify patterns, trends, and correlations. By leveraging Big Data Analytics, these algorithms can generate accurate demand forecasts, production schedules, and inventory optimization strategies. This predictive capability helps businesses anticipate market demand, optimize resource allocation, and minimize stockouts or overstock situations.
  2. Optimized Supply Chain Management: Generative AI algorithms combined with Big Data Analytics can optimize supply chain processes by analyzing data from various sources such as suppliers, manufacturers, distributors, and customers. These algorithms can identify inefficiencies, bottlenecks, and risks in the supply chain, allowing businesses to streamline logistics operations, reduce costs, and improve delivery performance.
  3. Real-time Monitoring and Decision-making: Industry 4.0 technologies such as Internet of Things (IoT) sensors, RFID tags, and smart devices enable real-time monitoring of production and logistics processes. Generative AI algorithms can analyze streaming data from these devices to detect anomalies, predict equipment failures, and optimize route planning in real-time. This real-time visibility and decision-making capability empower businesses to respond quickly to changes in demand, supply, or market conditions.
  4. Dynamic Routing and Transportation Optimization: Generative AI algorithms can optimize transportation routes, vehicle scheduling, and load balancing based on real-time data and predictive analytics. By considering factors such as traffic conditions, weather forecasts, fuel efficiency, and delivery deadlines, these algorithms can generate optimal routing plans that minimize transportation costs and maximize resource utilization.
  5. Continuous Improvement and Adaptability: Generative AI technology combined with Big Data Analytics enables continuous improvement and adaptability in production logistics management processes. By analyzing performance metrics, customer feedback, and market trends, businesses can identify opportunities for optimization, innovation, and process refinement. These insights drive continuous improvement initiatives, allowing businesses to stay agile, competitive, and responsive to changing market dynamics.
  6. Risk Management and Resilience: Generative AI algorithms can assess supply chain risks and vulnerabilities by analyzing data from multiple sources, including geopolitical factors, natural disasters, supplier disruptions, and regulatory changes. By proactively identifying risks and implementing mitigation strategies, businesses can enhance supply chain resilience, minimize disruptions, and ensure business continuity.
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Hello everyone,
I have research including two objectives,
one of them is to assess the relationship using logistic regression.
another one is comparing two groups using Mann-Whitney U Test.
if I want to apply sample size formulation need to calculate separately for each objective?
also what is the Minium sample size for logistic?
Thanks.
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Thank you for clarifying, Bahar Ysr. PASS has routines for both logistic regression and the Wilcoxon-Mann-Whitney test. So I would estimate the needed sample sizes for both and then choose the larger of the two.
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Hi everyone,
I'm conducting research on the impact of AI on workforce displacement in the logistics industry for my Bachelor's thesis. If you work in logistics and have experience with AI technologies, your insights would be valuable. Interested in participating in a short survey?
Thank you for your interest!
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Assalamu Alaikum
With due respect, I am interested in perticipating your survey.
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Why parsimoniously does fertility negatively correlate with socioeconomic status? How?
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Overall, the negative correlation between fertility and socioeconomic status can be attributed to a combination of economic, educational, cultural, and structural factors that shape individuals' reproductive choices and opportunities. Understanding these mechanisms is essential for policymakers, healthcare providers, and social scientists seeking to address disparities in reproductive health outcomes and promote equitable access to family planning resources.
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Clinical > book. Therefore, if a barber was trained and had a track record of successfully giving a very specific surgery then, is more likely to succeed than an MD who only has read books without clinical experience.
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Neat idea, how about letting or certifying someone just in simple antibiotics and Corticosteroid pills (usually prednisone)? For example, for severe rashes like poison ivy are always of prednisone and go home. You could easily make a list of 20 common ailments and their protocols that are almost knee-jerk reactions.
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What are the Logistical and Administrative Challenges in implementing open book examination?
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First of all, we can't simply convert all exams to open-book exams. It depends on how the course is relevant to data-driven, case-based, statistics, infographics, or calculative ones. It is better to go for an open book exam for such a course of study otherwise performance can't be expected as the scale of teaching delivered in the exam.
On the logistics and administrative challenges concern - it is very tough to manage by invigilators for being long hours exams, so manning issues will come, maybe more cheating chances will come in place, exchanges of notes, discussion will happen, you cant keep eyes on everyone etc.
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I’ve been trying to login multiple times but, I keep failing.
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1. Technical problems: The platform might be going through maintenance or having technical problems, which could interfere with the login procedure. It is best to ask for help from the platform's support staff in such circumstances.
2. Incorrect login information: Verify that the username and password you are entering are correct. In the event that you lose your login information, you can typically utilise the platform's password recovery feature to get back in.
3. Account activation: To use certain platforms, users must go through a verification process, like clicking on an email link, to activate their accounts. Your ability to log in may be blocked if the activation process is not finished.
4. Browser or device compatibility: Some platforms might need particular web browsers or devices to work properly, or they might have compatibility problems. To see if that fixes the login problem, try utilising a different browser or device.
5. Security measures: A platform may occasionally make the login process more difficult if it has added extra security measures like two-factor authentication (2FA). In order to successfully finish the authentication process, make sure you adhere to the given instructions.
For help, I suggest contacting Qeios customer support if you are having trouble logging in consistently. They will be able to answer any questions you might have and offer you specific advice.
@salomebukachi
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I have thought to cover the challenges faced by various functions in managing the supply chain - Procurement, Quality Assurance, Technical, Logistics , Planning and Regulatory
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ResearchGate Link:
(PDF) Improving Lean engagement through utilising improved communication, recognition and digitalisation during the COVID-19 pandemic in JLR's powertrain machining facility (researchgate.net)
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What are the logistical and financial difficulties?
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I am satisfied by my career of scientific research, there are many difficulties but the satisfaction is when you can manage it and arrive to you objective
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Hegel's Science of Logic involves a trinary process: understanding -> dialectical reasoning -> speculative reasoning -> understanding at a higher level. This is analogous to a period 3 orbit, a return to self mediated by the other A -> B -> C -> A.
A direct consequence of a really strange theorem in dynamical systems due to Sharkovskii is that for any continuous function on the reals f: R -> R, if f has an orbit of minimal period 3 then it has orbits of any minimal period (because 3 is the first number in the Sharkovskii ordering). This happens in particular for certain values of lambda for the logistic map, exhibiting the phenomenon of 'chaos'.
The graph of successive higher iterations of the logistic map exhibit self-similarity, a fractal-like nature. This again mirrors the structure of the logic in which each part is similar in its trinary structure to that of the whole, i.e. Being -> Essence -> Notion, but in Being we have Quantity -> Quality -> Measure. In Concept we have Subjective Notion -> Objective Notion -> Absolute Notion. At a further finer level inside Quantity we have for instance Pure Being -> Nothing -> Becoming, etc.
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I can't help remembering that according to Arthur C. Clarke 'the Ramans do everything in threes'.
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Hello,my name is Chaimae and my thesis topic is as follow: Management of large distribution logistics in Morocco.
Please tell me what do you think about this subject, wheter it is relevent and original and wether scientific reaserch has sufficiently addressed it. I need youd advices.
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Thank you
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This is my thesis topic:Management of logistics in the large distribution sector in Morocco.
And Here is my prob.: To what extent do the operational strategies developed and policies adopted by promoters of large distribution in Morocco allow for competitively managing the logistics chain of large distribution?
And here are the given hypotheses:
• Hypothesis 1: Close collaboration with suppliers could secure supplies, resolve the plethora of supply chains, guarantee merchandise returns, and professionalize logistics personnel.
• Hypothesis 2: Investing in human resources could enhance the distribution chain's supply, storage performance, order preparation, and shipment, fostering staff loyalty and better customer service.
• Hypothesis 3: Inadequate stock positioning in the logistics chain, improper facility localization, and their inflexibility to support a wide range of products and varying volumes might be the cause of the lack of competitiveness among large distribution companies.
• Hypothesis 4: Poor stock management could be the reason behind high logistics costs in large distribution.
• Hypothesis 5: Poor order preparation and shipment conditions might be among the main causes of mismanaging the customer experience.
What do you think? Your comments? Is this a genuine issue? What could I add or remove?"
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thank you so much
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The results of my logistic analysis research show that the pseudo rsquare is only 0.07. Can I still continue my research?
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technically, this should make sense if you compare two R-squared values of the same type (of either linear regression or logistic regression models, however, different models) on the same dataset, predicting the same outcome ...
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Hi, I'm doing scientific research about "The impacts of green logistics performance on economic growth: Evidence from China". This is my first time doing research, so my idea gets troubles a lot, I want to ask you guys some questions.
Firstly, about my idea, I am having problems collecting logistics activities data. I don't know whether the data I collect is in a country that has applied green logistics or not because it is only written as "logistic performance index".
Secondly, my instructor wondered why I chose China (in my opinion, because China emits a lot of CO2 so it will be easy to evaluate the impact of applying green logistics on the economy), but she told me that it is not the case, so I have to consider about it again.
It would be great if you could answer ( any websites to collect data..) and give me directions for this idea, I'm a freshman in this field :(. Thank you so much
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That is a really interesting question and i tries to work on a similar product based problem during my bachelors and here is just an opinion based on my experience-
1) You need to divide the emissions on the basis of Scope 1,Scope 2 and Scope 3 and if we think of this problem not as a logistic proboems but as a supply chain problem.
2) The data for scope 3 emissions firstly is not available, secondly the available one is not reliable, this is the main problem.
3) Even though scope 3 emissions data is not available as a single entity but now lets divide it into categories - air, water and land. Now try to find a country which uses air as its major form of transportation and not water or land because emissions from air logistics can still be tracked and accuntable data can be found, then maybe using that data to somehow figure how much other modes are emitting.
Now the reason why i am suggesting this - Even though countries like China and India emit a lot but remember that these are energy and manufacturing emissions, logistics emissions are extremely small as these countries have already adapted to railwasy which in the case of india are running on 97% electricity only (green logisticcs) and railways accounts for a major chunk of logistics) and compare this with a country like Green land where the only reliable mode of transportation is planes.
I hope i was able to help you
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Como se maneja la big data en el proceso logistico en una organizacion, cuales son los avances de los ultimos años
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If you ask chatGPT "How is big data managed in the logistics process within an organization, and what are the recent advances?" a reasonable answer comes up:
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Managing big data in the logistics process within an organization has become increasingly critical in recent years. Here are some of the recent advances in this field:
1. **Real-time Data Analysis:** Logistics companies are now using big data analytics to process and analyze data in real-time. This allows them to make decisions quickly, optimize routes, and respond to changing conditions on the fly.
2. **Predictive Analytics:** Advanced algorithms and machine learning models are being used to predict future logistics trends, demand patterns, and potential disruptions. This helps in proactive decision-making and resource allocation.
3. **IoT Integration:** Internet of Things (IoT) devices, such as GPS trackers and sensors, are extensively used to collect data from vehicles, products, and facilities. This data is then integrated into big data systems to provide a holistic view of the supply chain.
4. **Blockchain Technology:** Blockchain is being explored for enhancing transparency and security in logistics. It helps in tracking goods through the supply chain, reducing fraud, and ensuring data integrity.
5. **Automation and Robotics:** Automation and robotics are increasingly used in warehouses and distribution centers. These technologies generate large amounts of data that can be harnessed for optimization and efficiency.
6. **Cloud Computing:** The adoption of cloud-based solutions has made it easier for organizations to store and process vast amounts of data. This scalability is essential for handling big data effectively.
7. **Data Visualization Tools:** User-friendly data visualization tools make it easier for logistics professionals to interpret and gain insights from big data, facilitating better decision-making.
8. **Environmental Considerations:** Logistics companies are using big data to optimize routes and reduce the environmental footprint by minimizing fuel consumption and emissions.
9. **Supplier and Inventory Management:** Big data analytics help in improving supplier relationships and optimizing inventory levels, reducing costs, and ensuring products are available when needed.
10. **Customization and Personalization:** Big data allows logistics companies to tailor services and products to individual customer preferences, providing a more personalized and efficient experience.
In summary, the management of big data in the logistics process has seen significant advancements in recent years, contributing to more efficient and responsive supply chains, cost reductions, and improved customer experiences. The ability to collect, process, and analyze data has become a crucial competitive advantage in the logistics industry.
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I would add that the biggest problems that companies face is attempting to exploit recent advances is lack of software development skills. The reason is as follows:
1. To exploit new technology the skill must be there to create a test and verify the benefit of the advancement.
2. To test a technology that requires big data as input, big data administration skills are a prerequisite.
3. As these skills are novel (big data is only 10 years old) organisations don't hire enough SW engineers to enable or managing experiments at this scale.
So what are these skills? Again ChatGPT provides a reasonable answer:
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Managing big data effectively requires a combination of technical, analytical, and business skills. Here are some of the key skills required to manage big data:
1. **Data Analysis:** Proficiency in data analysis is crucial. You should be able to extract meaningful insights from large datasets. Familiarity with tools like Python, R, and data analysis libraries is essential.
2. **Data Management:** Understanding data storage and management systems is vital. This includes knowledge of relational databases, NoSQL databases, and data warehousing solutions.
3. **Programming Skills:** Proficiency in programming languages, particularly Python and Java, is important for data manipulation, transformation, and analysis. Knowledge of SQL for database querying is also valuable.
4. **Big Data Technologies:** Familiarity with big data technologies such as Hadoop, Spark, and related ecosystems is essential. These tools enable the processing and analysis of large datasets.
5. **Machine Learning:** Basic understanding of machine learning is beneficial, as it can be applied to extract predictive insights from big data. Knowing how to use machine learning libraries and frameworks is an advantage.
6. **Data Visualization:** The ability to create meaningful visualizations using tools like Tableau, Power BI, or open-source options is crucial for conveying insights to non-technical stakeholders.
7. **Statistics:** A good grasp of statistical methods is essential for data analysis and interpretation. Knowledge of descriptive statistics, inferential statistics, and hypothesis testing is valuable.
8. **Business Acumen:** Understanding the business context is critical. Being able to translate data insights into actionable strategies and decisions that benefit the organization is a key skill.
9. **Data Security and Compliance:** Knowledge of data security best practices and compliance regulations is important, especially when handling sensitive or regulated data.
10. **Problem-Solving:** Strong problem-solving skills are required to address data-related challenges, such as data quality issues, scalability, and performance optimization.
11. **Communication:** Effective communication is crucial for conveying complex technical findings to non-technical stakeholders and decision-makers.
12. **Project Management:** Being able to manage big data projects, including setting objectives, timelines, and resources, is important for successful data management.
13. **Data Engineering:** Understanding data engineering concepts, including data pipelines, data cleansing, and ETL (Extract, Transform, Load) processes, is essential for data preparation and transformation.
14. **Cloud Computing:** Familiarity with cloud platforms like AWS, Azure, or Google Cloud is valuable, as many big data solutions are hosted in the cloud.
15. **Continuous Learning:** Given the rapidly evolving nature of data technologies, a willingness to continually learn and adapt to new tools and techniques is essential.
In summary, managing big data requires a combination of technical, analytical, and business skills. As the field continues to evolve, staying updated with the latest developments and tools is essential to be an effective big data manager.
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Whilst chatGPT lists all the general issues, it is important to recognize that reducing the number of technologies will significantly reduce the cognitive burden of the big data team. Unfortunately, and due to the rapid development of technologies navigating all the possible technologies leads to a jungle of options, where selection of tools for "big data" projects becomes critical. Statistics by Alex Yu (ByteByteGo) reveal that more than 50% of big data projects fail to meet expectations due to wrong choice of technology (e.g. poor tech stack choice) and hence require some form of re-engineering that causes project overrun.
In summary, the lack of skills and previous experiences leaves big data projects to the largest corporations which can afford the learning process.
Finally I recommend you to ask yourself: What makes big data "big"?
A sufficiently technical answer to that question will leave you informed about what is required to manage "big data" and subsequently "how to manage" to prerequisites.
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As we look towards a more sustainable and ethical future, one of the most exciting developments in the world of food is cultivated meat. 🌎🍖
🔬 What is Cultivated Meat? Cultivated meat, also known as lab-grown or cell-based meat, is produced by culturing animal cells in a controlled environment, without the need to raise and slaughter animals. It offers the promise of a more sustainable, humane, and environmentally friendly way to meet our growing global demand for meat.
🌍 Why is it Important?
  • Sustainability: Did you know that the meat industry contributes to approximately 15% of global greenhouse gas emissions annually? As meat consumption continues to rise, so does its impact on the environment. Cultivated meat requires significantly fewer resources, such as land and water, and produces up to 90% less greenhouse gas emissions compared to traditional livestock farming.
  • Animal Welfare: It eliminates the need for factory farming, where millions of animals endure harsh conditions. By embracing cultivated meat, we can reduce animal suffering on an unprecedented scale.
  • Food Security: With the world's population expected to reach 9 billion by 2050, meat consumption is projected to double. Cultivated meat could play a crucial role in ensuring food security while reducing the pressure on our planet's resources.
💲 Price Comparison: Currently, cultivated meat is more expensive to produce than conventionally farmed meat, with prices ranging from approximately $50 to $150 per kilogram. In contrast, the cost of traditional meat production can vary widely but is generally lower.
📈 Future Price Trends: The future of cultivated meat pricing is promising. As technology advances and economies of scale come into play, we can expect the price of cultivated meat to decrease significantly. Projections indicate that it could become cost-competitive with traditional meat within the next decade, making it a viable and sustainable choice for consumers worldwide.
🌟 Engineering Challenges: The development of cultivated meat involves intricate engineering challenges:
  • Bioreactor Design: Creating efficient bioreactors for cell culturing and growth is crucial. Engineers are working on scalable designs to optimize cell proliferation.
  • Nutrient Formulation: Designing the perfect nutrient solution to support cell growth, mimicking the natural environment, is a complex task.
  • Scaffold Materials: Innovations in scaffold materials are vital for replicating the texture of traditional meat.
  • Scaling Production: Transitioning from lab-scale production to large-scale manufacturing presents logistical and technical hurdles.
🧪 Scientific Advancements:
  • Cell Lines: Scientists are continually improving cell lines to enhance flavor and texture, ensuring cultivated meat is indistinguishable from conventionally farmed meat.
  • Culturing Techniques: Developing advanced culturing techniques like 3D bioprinting for complex meat structures is a priority.
  • Sustainability Metrics: Research is ongoing to quantify the precise environmental benefits of cultivated meat production.
💡 Let's Collaborate: I believe in the power of innovation to shape a better future. Whether you're a scientist, entrepreneur, or simply curious about the future of food, let's connect and discuss how we can contribute to this exciting journey.
🌱 Let's Keep the Conversation Going: The potential benefits of cultivated meat are vast, from reducing greenhouse gas emissions to safeguarding our planet's ecosystems. Share your thoughts on these environmental impacts and the groundbreaking technology of cultivated meat in the comments below!
#CultivatedMeat #Sustainability #FutureOfFood #Innovation #FoodTech #EngineeringChallenges #MeatPrices
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Thanks Robert for your thoughtful response. You've brought up some valid points that highlight the complexity of assessing the environmental and ethical aspects of cultivated meat production. Let's address your concerns one by one:
  1. Carbon Emissions from Reagents: You're absolutely right that the carbon emissions associated with the production of cell culture reagents, which are derived from the petrochemical industry, should be considered when evaluating the overall environmental impact of cultivated meat. It's essential to have a comprehensive life-cycle analysis that takes into account all such factors for a more accurate assessment.
  2. Methane vs. CO2 Emissions: The comparison between methane and carbon dioxide emissions is a crucial one, and you rightly point out that reducing methane emissions from livestock is a viable strategy. This is especially true for ruminant animals like cows. However, cultivated meat still has the potential to significantly reduce the overall greenhouse gas emissions compared to traditional livestock farming, even when considering changes in livestock farming practices.
  3. Resource Use for Cultured Meat: It's important to recognize that, as of now, some resource inputs are still required for cultivated meat production, such as the cultivation of cells and the production of growth media. While it may not be a perfect solution, the potential for more efficient resource use and lower environmental impact is significant. The goal is to continually improve and optimize these processes to minimize their impact.
  4. Animal Welfare: You rightly mention that animal welfare concerns are often more about the methods used in the production chain rather than the process itself. However, cultivated meat, by eliminating the need for raising and slaughtering animals on a large scale, can still have a positive impact on reducing animal suffering and addressing welfare issues inherent in traditional livestock farming.
It's essential to acknowledge that cultivated meat is not a perfect solution, and there are challenges and limitations that need to be addressed. It's a rapidly evolving field, and ongoing research and innovation are critical to overcome these challenges and make cultivated meat a more sustainable and ethical alternative to conventional meat production. Collaboration among scientists, entrepreneurs, and interested individuals, as you've suggested, is vital to finding solutions and continuously improving this exciting technology for a better future.
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How can data science and statistical analysis be used to improve the shipping and logistics industry?
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Just like any industry there should be national or state-based reports, as per hospital industry has hospital admissions data in the AIHW reports, there should be a like document for shipping related to statistics collected ie no of ships docked, containers unloaded, weight of shipping containers, products dumped due to contamination etc.
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I have measured Irisin levels in plasma and now I'm trying to analyze the results. As far as I have read, I need to perform a 4 parameter logistic regression, should I use logarithm for absorbances and for concentrations?
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I use the program Prism GraphPad for that. The data used is only the concentration level of the standard curve and the optical density levels. Then I interpolate the values for the estimation of the concentration of the protein in my samples. After selecting the interpolate option, a parameters window is open, where I select the 4 parameter logistic regression.
I hope this can help you.
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I am trying to formulate a research title. Seeking expert advice.
(1) What could be the popular area of doing research in the field of Logistics Education, Port performance, Last Mile delivery, Block Chain?
(2) Any suggestion for the possible research gap in these areas?
(3) Any specific title you like to suggest?
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Currently digital transformation in these areas is a hot topic and it can link parts of your fields (see, e.g., ). Moreover, digital twins are a recent research topic in these fields and there are quite a few developments. Developing a serious game supporting digital transformation might be a possible focus and the titel should possibly start with the word integration or similar. The development of integrated mathematical models to improve / solve problems in ports is a nice topic with various research gaps.
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I need a dataset based on blockchain and logistics supply chain
to help me in my PhD thesis
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Certainly! 📦🔗 Here's a digital treasure hunt: explore academic databases like IEEE Xplore, ACM Digital Library, and arXiv for "Blockchain in Logistics" gems. Unearth data diamonds to fuel your PhD quest. Happy hunting! 🚀🔍
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R4TLI 2023 extended abstract submission site is open.
Extended Abstract Submission Deadline – 30th April 2023
The Sri Lanka Society of Transport and Logistics (SLSTL) is organizing the 8th International Conference on Research for Transport and Logistics Industry (R4TLI) 2023, which is scheduled to be held on the 26th of August 2023 in Colombo, Sri Lanka both onsite and virtually.
R4TLI is an initiative that endeavors to promote Research and Development in the Transport and Logistics Industry in Sri Lanka, and in the Asian region helping diagnose its current issues, experienced by the industry and identify potential improvements and sustainable solutions, based on scientific evidence. This annual research conference intends to build a platform for effective dialogue among academics and professionals in the fields, and for showcasing of research outputs and dissemination of new knowledge developed in this regard.
We encourage all of you, academics and professionals interested in the research and development of the transport and logistics industry to join us by submitting Extended Abstracts (the extended abstracts template is attached herewith) on your authentic research, in view of presenting them at the conference and subsequently publishing them in the R4TLI Proceedings. (For past proceedings visit “PUBLICATIONS” menu at www.slstl.lk)
The selected Extended Abstracts will be invited to submit the full paper for the Journal of South Asian Logistics and Transport (JSALT) which is a peer-reviewed fully open-access Journal of the Sri Lanka Society of Transport and Logistics, Moratuwa, Sri Lanka and published by SLJOL (https://jsalt.sljol.info/)
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Please give more detailed information about the conference, the conditions for participation in it.
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How shipping and logistics industry can be improved using Data science and Statistical analysis methods?
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Here are some ways in which these technologies can be leveraged to improve the efficiency and effectiveness of the industry:
  1. Route Optimization: Data science can analyze historical shipping data, traffic patterns, weather conditions, and other relevant factors to optimize shipping routes. This can reduce transit times, fuel consumption, and overall costs while improving delivery reliability.
  2. Demand Forecasting: By utilizing statistical analysis methods, logistics companies can accurately forecast demand for their services. This enables them to plan their resources and capacity effectively, avoiding overstocking or understocking inventory.
  3. Real-time Tracking and Visibility: Data science can facilitate real-time tracking of shipments, providing better visibility to logistics managers and customers. This can lead to more accurate delivery estimates, enhanced customer service, and the ability to proactively address potential delays or issues.
  4. Predictive Maintenance: Applying data science techniques to monitor equipment and vehicles can help identify potential maintenance issues before they lead to breakdowns. This predictive maintenance approach minimizes downtime, reduces repair costs, and improves overall fleet efficiency.
  5. Warehouse Optimization: Statistical analysis can optimize warehouse layouts, inventory management, and order picking processes. By analyzing historical data, logistics companies can identify trends and patterns, leading to better storage decisions and streamlined operations.
  6. Risk Management: Data science can be used to assess and mitigate risks associated with shipping and logistics, such as accidents, theft, or natural disasters. Predictive modeling can help identify high-risk areas and develop strategies to reduce potential losses.
  7. Last-Mile Delivery Efficiency: Data science can optimize last-mile delivery routes by considering various factors, including traffic conditions, customer locations, and delivery preferences. This can result in reduced delivery times and improved customer satisfaction.
  8. Cost Optimization: Through statistical analysis of shipping data and associated costs, logistics companies can identify areas for cost reduction and operational improvements. Data-driven decision-making can lead to significant savings across the supply chain.
  9. Supply Chain Optimization: Data science can analyze the entire supply chain, identifying bottlenecks and inefficiencies. By optimizing the supply chain through data-driven insights, companies can reduce lead times, inventory levels, and transportation costs.
  10. Environmental Impact Reduction: Data science can aid in minimizing the environmental impact of shipping and logistics operations by optimizing routes to reduce carbon emissions, adopting more fuel-efficient transportation options, and implementing sustainable practices.
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What are the skills required in logistics field in Africa?
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The logistics industry in Africa requires a diverse set of skills to effectively navigate the unique challenges and opportunities. Some of the key skills required in the logistics in Africa include:
1. Supply Chain Management: Understanding the end-to-end supply chain, including procurement, transportation, warehousing, and distribution, is essential. Proficiency in managing inventory, demand forecasting, and optimizing logistics processes is crucial.
2. International Trade and Customs: Given the international nature of logistics, knowledge of trade regulations, customs procedures, and import/export documentation is vital. Understanding trade agreements, tariffs, and compliance requirements facilitates smooth cross-border operations.
3. Transportation and Fleet Management: Efficient transportation is critical in Africa, considering the diverse infrastructure and geographical challenges. Proficiency in managing different modes of transportation, optimizing routes, and maintaining an effective fleet is necessary.
4. Warehousing and Inventory Management: Skills in warehouse operations, space utilization, inventory control, and order fulfillment are important for effective logistics management. Knowledge of warehouse layout, storage systems, and inventory tracking methods helps streamline operations.
5. Information Technology and Systems: Proficiency in using logistics management software, transportation management systems (TMS), warehouse management systems (WMS), and enterprise resource planning (ERP) systems is essential. Data analysis, reporting, and leveraging technology for process optimization are crucial skills.
6. Negotiation and Relationship Management: Building strong relationships with suppliers, customers, freight forwarders, and other logistics partners is crucial. Negotiation skills, effective communication, and the ability to collaborate across different stakeholders are important for successful logistics operations.
7. Problem-solving and Decision-making: The logistics field often involves unforeseen challenges and requires the ability to think critically, make quick decisions, and solve problems effectively. Adapting to changing situations and finding innovative solutions is important.
8. Cultural Awareness and Adaptability: Africa is a diverse continent with various cultures, languages, and business practices. Having cultural sensitivity, adaptability, and the ability to work in cross-cultural environments is valuable.
9. Financial Management: Understanding cost analysis, budgeting, and financial management principles is essential to optimize logistics operations and ensure profitability.
10. Environmental and Sustainability Awareness: Recognizing the importance of sustainability and environmental considerations in logistics is becoming increasingly important. Knowledge of sustainable practices, green logistics, and circular economy principles can be advantageous.
These skills provide a foundation for success in the logistics not only in Africa but also other parts of the world. However, it's important to note that the specific skill requirements may vary depending on the industry, sector, and job role within logistics. Continuous learning, staying updated with industry trends, and adapting to the evolving logistics landscape are also crucial for professional growth in this field.
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How to define a quantum circuit of the quantum baker map scrambling? I read the articles based on quantum baker map scrambling : (1) Quantum image encryption based on baker map and 2D logistic map. (2) Quantum image scrambling algorithm based on discrete baker map. In these articles they mentioned the circuit but how did they get it? Anyone, please help me to tackle this issue?
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Designing a quantum circuit for the quantum baker map scrambling involves several steps. Firstly, you need to understand the mathematical description of the discrete baker map and its quantum analog. Then, you can define the necessary quantum gates and operations required to implement the map. This typically involves using qubit rotations, controlled operations, and ancillary qubits. The specific circuit design may vary depending on the details mentioned in the articles you referenced. To tackle this issue, it is recommended to carefully study the articles, analyze the circuit diagrams provided, and comprehend the transformations applied in each step. Additionally, consulting with experts in the field or reaching out to the authors of the articles for clarification can also be helpful in understanding and replicating the circuit design.
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Hello everyone, please I am a masters student of international Logistics and operation management and currently starting my dissertation, I will like To get dissertation topic on SAP. Can anyone assist me on dissertations topics related? Can I also get data on this topic? My supervisors wants me to write something unique and standout from all other students.
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Dear Joy: Try exploring these
Evaluating the Impact of SAP Implementation on Supply Chain Performance: A Comparative Analysis of Companies in Different Industries"
Optimizing Warehouse Management through SAP Integration: A Case Study of [Company/Organization]
Leveraging SAP Analytics for Demand Forecasting and Planning in Global Supply Chains
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Currently, the RGPPM technique is being focused on measuring the carbon footprint during the delivery of merchandise in last-mile logistics. However, it is important to establish KPIs related to the carbon footprint for carrying out comparisons with current methodologies or techniques in industry or academia.
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There are several methodologies and techniques that can be used to measure the carbon footprint of last-mile logistics. Here are a few examples:
1. Emission Factors: This approach is based on using emission factors that are published for different modes of transportation, such as delivery vans or bicycles. Emission factors are used to estimate the amount of CO2 emissions based on the distance travelled and the mode of transportation used.
2. Life Cycle Assessment (LCA): LCA is a technique that evaluates the environmental impact of a product or service throughout its entire life cycle, from raw material extraction to disposal. LCA can be used to identify the carbon footprint of last-mile logistics by considering the emissions associated with the production, distribution, use, and end-of-life of the vehicles and equipment used for delivery.
3. GPS Tracking: GPS tracking technology can be used to track the routes and distances travelled by delivery vehicles. This data can be combined with emission factors to estimate the amount of CO2 emissions generated during the last-mile delivery process.
4. Simulation Models: Simulation models can be used to estimate the carbon footprint of last-mile logistics by simulating the delivery process and estimating the energy consumption and emissions associated with each step.
5. Carbon Accounting Tools: There are a number of carbon accounting tools available that can be used to estimate the carbon footprint of last-mile logistics. These tools use data inputs such as distance, mode of transportation, and fuel consumption to calculate the carbon emissions associated with last-mile delivery.
Overall, the choice of methodology or technique will depend on the specific context and data availability. It may be necessary to use a combination of approaches to obtain a comprehensive and accurate estimate of the carbon footprint of last-mile logistics.
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Please help me with any sops, published literature or any readers manual regarding logistics and resources required to set up a TDM unit.
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There are some resources and logistics that may be required:
1. Equipment: A laboratory equipped with the necessary instruments and reagents for drug analysis.
2. Trained personnel: Skilled professionals such as pharmacists, clinical chemists, and laboratory technicians who are trained in therapeutic drug monitoring.
3. Quality control measures: Standard operating procedures (SOPs) for sample collection, handling, storage, and analysis to ensure accuracy and precision of results.
4. Accreditation: Accreditation from regulatory bodies such as the Clinical Laboratory Improvement Amendments (CLIA) or the College of American Pathologists (CAP) may be required.
5. Information technology infrastructure: A laboratory information management system (LIMS) to manage patient data and test results.
6. Collaboration with healthcare providers: Collaboration with physicians or other healthcare providers who can order tests and interpret results is essential.
7. Financial resources: Funding for equipment, personnel salaries, quality control measures, accreditation fees, and other expenses associated with setting up a therapeutic drug monitoring service.
8. Legal considerations: Compliance with local regulations regarding laboratory testing and patient privacy laws is crucial.
9. Marketing strategy: Developing a marketing strategy to promote the service to potential clients such as hospitals or clinics may also be necessary.
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I would like to get the information regarding real data on transportation. How can I get that particular data.
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There are several ways to obtain real data on transportation:
1. Government websites: Many government agencies collect and publish data on transportation, such as the U.S. Department of Transportation or the European Union's Eurostat. These websites often provide access to datasets, reports, and other resources related to transportation.
2. Private companies: Some private companies collect and sell transportation data, such as INRIX or TomTom. These companies may offer access to real-time traffic information, travel times, and other data related to transportation.
3. Research institutions: Universities and research institutions often conduct studies on transportation-related topics and publish their findings in academic journals or reports. These publications may include data on traffic patterns, public transit usage, and other aspects of transportation.
4. Open data portals: Many cities and countries have open data portals that provide access to a wide range of datasets, including those related to transportation. Examples include New York City's Open Data Portal or the UK's Data.gov.uk.
5. Surveys: Surveys can be conducted by government agencies or private organizations to gather information about travel behavior, commuting patterns, and other aspects of transportation. These surveys may be available online or in-person through phone interviews or mail surveys.
Overall, there are many sources of real data on transportation available for researchers, policymakers, and others interested in this.
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Hello,
I am modeling bacterial growth using a logistic growth function:
X' = μX(1-N/κ),
where B' is the increment in bacterial population, μ the specific growth rate, N the total bacterial density, and κ the carrying capacity.
I would like to use the Gompertz model, instead, but I am not sure if I can use the parameters of the logistic model. The parameters for the Gompertz are:
L(t) = AᴮCexp(-e(-B(t-M)))
where:
L(t) is the log of the number of bacteria at time ‘t’ (in hour);
A is the asymptotic log of number of bacteria as ‘t’ decreases indefinitely;
C is the asymptotic amount of growth (log number) that occurs as ‘t’ increases indefinitely;
M is the time (in hour) at which the absolute growth rate is at maximum;
and B is the relative growth at time ‘M’.
What parameters of the logistic can I use in the Gompertz model? Is A = κ, for instance?
Thank you
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Just put your data in this software and you will get the Gompertz parameters:
Let me know if you need assistance.
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With greater emphasis being placed on ‘re-use’ and ‘recycle’,how will future supply chains manage the need to incorporate ‘reverse flows’ and ‘circular logistics’?
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The recyclability or reusability of the material and whether it will re-enter the same supply chain are factors that determine its suitability for reverse logistics. Nonetheless, reverse logistics is closely linked to the circular economy and closed-loop supply chains.
here is a research paper, which redesigned Agri supply chain to integrate circularity within food supply chains
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I need information about Amazon logistics
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Field of Operation: Amazon is an American multinational technology company that specializes in e-commerce, digital streaming, cloud computing, and artificial intelligence. It is one of the world's largest online retailers, offering a wide range of products and services to customers worldwide. In addition to its core retail operations, Amazon also operates subsidiaries such as Amazon Web Services (AWS), Amazon Studios, and Amazon Robotics.
Transportation System: Amazon operates a highly efficient and sophisticated transportation system to support its e-commerce operations. The company uses a combination of in-house and third-party logistics providers to handle the movement of goods from its fulfillment centers to customers around the world. Amazon's transportation network includes a vast fleet of delivery vans, trucks, and planes, as well as partnerships with major shipping carriers such as UPS, FedEx, and the United States Postal Service.
Trade Conditions: As a major player in global e-commerce, Amazon operates in a highly competitive and complex trade environment. The company is subject to various regulations and trade agreements that govern cross-border trade, including tariffs, customs duties, and import/export regulations. Amazon has also faced criticism and regulatory scrutiny over issues such as labor practices, privacy concerns, and antitrust violations. Additionally, the company has been impacted by the COVID-19 pandemic and related disruptions to global supply chains and logistics networks.
I hope this information helps with your documentation. Let me know if you need any further assistance.
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The equation dx/dt = F(x) can be linearized using Calerman techniques and solved with linear state equation method. But for some condition I found a proper decomposition of F(x) relating to know logist solution od a foundamental canonical problem .
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Hello from 2016 up today i have finally solved any non linear autonomous first orde diff equations . The solution is in terms of important analysical recursion which i will present in a later paper. Regards.
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Dear Statisticians,
I was running binary logistics and this is the output report I get it. The confidence interval looks unusual and I do not understand the meaning of it. Could you help me interpret please? kINDLY refer the file attached for reference.
Have a great days ahead.
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Your extremely high values induce much numerical error. Consider dividing your observables by some scale factor to bring them close to the magnitude of 1, and then repeat your statistical analysis on those normalized observables.
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Dear Colleagues,
Thanks for your prompt responses. But if we plot bifurcation diagram, lypanuov exponent and tragectory plot for function rx(2-3x) it shows different behaviour. So, it attracted my attention to do work on it. The bifurcation diagram of rx(2-3x) suggests that there is no bifurcation but only a little discrete chaos has rate of convergence good as compared with logistic map. If anyone interested in doing work on this new scheme, please send me the email through which we can communicate and whatsapp number also, so that frequency of interaction shall be increased and neck to neck traversing of results can be made jointly.
Manish
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Use two-parameter family in this case as: 2rx(1-1.5x), replace 1.5 by another parameter. You can do iterations like this:
or
I hope that it will help.
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We are well familiar with Logistic Map rx(1-x) but can anyone has the idea about rx(2-3x) map or rx(3-2x). If anyone has the study of these mappings, please familiarise me with that study and if previously in the literature this study does not exists, please help me to study this phenomenon also.
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Dear Colleagues,
Thanks for your prompt responses. But if we plot bifurcation diagram, lypanuov exponent and tragectory plot for function rx(2-3x) it shows different behaviour. So, it attracted my attention to do work on it. The bifurcation diagram of rx(2-3x) suggests that there is no bifurcation but only a little discrete chaos has rate of convergence good as compared with logistic map. If anyone interested in doing work on this new scheme, please send me the email through which we can communicate and whatsapp number also, so that frequency of interaction shall be increased and neck to neck traversing of results can be made jointly.
Manish
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Can anyone help me discuss how logistics in developing countries are going now
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Logistics is a very broad subject, as it determines the entire flow of goods. Many developing countries, yet, do not have a full net of distribution, as many business have grow more recently (when compared to developed countries).
The infrastructure to deliver goods with reliability and on-time is specially important on today's scenario where most of materials are planned just in time for optimization of capital usage.
Areas that face higher or uncertain lead times will have to mobilize more cash to keep the operation running and might end-up not being competitive/ on top of that poor logistic infrasctrucutres might be more expensive by itself and business that require more certainty on their production might end up expending more initial CAPEX to make sure suppliers are nearby.
You might read some more on this calculation on:
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What are some of the accessible yet trustworthy sources of secondary data in the field of logistics and supply chain management?
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There are several sources of secondary data in the field of logistics and supply chain management that are both accessible and trustworthy. Some examples include:
  1. Government agencies: Many government agencies, such as the United States Census Bureau and the United States Department of Transportation, collect and publish data related to logistics and supply chain management.
  2. Professional organizations: Professional organizations, such as the Council of Supply Chain Management Professionals (CSCMP) and the Institute for Supply Management (ISM), often publish reports and data on logistics and supply chain management trends and benchmarks.
  3. Industry associations: Industry associations, such as the National Retail Federation (NRF) and the National Association of Manufacturers (NAM), often publish data and reports on logistics and supply chain management issues that are specific to their industries.
  4. Market research firms: Market research firms, such as Gartner and Forrester, often publish reports and data on logistics and supply chain management trends and issues.
  5. Academic journals: Many academic journals, such as the Journal of Business Logistics and the Journal of Management, publish research on logistics and supply chain management topics.
It's important to carefully evaluate the credibility and reliability of any secondary data sources you use to ensure that the data is accurate and relevant to your research needs.
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can anyone please suggest matlab code for lyapunov exponent of logistic map
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sample Matlab code
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I wish to do a thesis on Service level on last mile logistics during covid. Which analysis tools i could use and why . I wish to investigate the reason behind rising costs.
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I think the answer would be many. As one of the respondent mention, cost benefit analysis is the one. In my opinion it would be good to give additional information on the nature and types of data you have collected? The analysis may depend on this. with my guess, you can apply regression analysis, TOBIT Probit analysis, logistic regression analysis, if at all you have huge data, why not ANN fitting.
Regards.
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I am attempting to perform a coIP after co-culturing cells and need to perform reversible crosslinking. I was thinking of using DSP and DTT to perform the reversible crosslinking. Has anyone performed this type of crosslinking on the two different cell lines? Trying to figure out the logistics on how to add the crosslinking reagent to both cells lines in their preferred growing conditions as the DSP cannot be added into the media.
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It is possible to perform reversible crosslinking using DSP (disuccinimidyl suberate) and DTT (dithiothreitol) on two different cell lines that have been co-cultured. However, there are a few challenges and considerations to keep in mind when using these reagents in this context.
One challenge is that DSP cannot be added directly to the growth media, as it can react with other components in the media and interfere with cell growth. Therefore, it will be necessary to remove the cells from the growth media and add the DSP to the cells in a separate solution. This can be done by gently washing the cells with a solution containing DSP, allowing the DSP to react with the cells for a specified amount of time, and then washing the cells again to remove any unbound DSP.
Another challenge is that DTT can inactivate DSP, so it will be necessary to add DTT to the cells after the DSP has been allowed to react. This can be done by washing the cells with a solution containing DTT, allowing the DTT to react with the cells for a specified amount of time, and then washing the cells again to remove any unbound DTT.
Overall, while it is possible to perform reversible crosslinking using DSP and DTT on co-cultured cells, it will be necessary to carefully consider the logistics of adding these reagents to the cells in their preferred growing conditions. It may also be helpful to consult with other researchers who have experience with this type of crosslinking to obtain guidance and advice.
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Hi How to test Heteroskedasticity and autocorrelation in Logistic Panel Regression?
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Why would you expect that it would be any different than than any other linear statistical model. Best wishes David Booth
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I am trying to identify the three strongest environments in the world for simulation and system dynamics in Operations, logistics and/or Supply chain management - can anyone suggest some environments?
With strong, I mean that have research groups, graduate trainings on the method and UTD-list scientific publications that applies it.
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One of the strongest for mentioned areas might be MIT Sloan System Dynamics Group:
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Like what is the impact factor of Cleaner Logistics and Supply Chain Journal ? Its publisher is Elsevier.
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Since the publisher is Elsevier, I highly doubt that these journals would not post their impact factor if they had any. Guessing from Cleaner Logistics, they are just too new (barely a year old) to compute an Impact Factor for them.
Please have a look at this Elsevier journal homepage to see where the impact factor would be: https://www.sciencedirect.com/journal/journal-of-power-sources
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I am finding it difficult to operationalize/ define last-mile logistics. As far as I understand there are several last-mile fulfilment options (conventional shopping, locker station etc.).
I believe last mile distribution encompasses last-mile delivery, and last mile fulfilment. How does last-mile logistics differ from these concepts?
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In early literature in humanitarian literature it was labeled as the 5 Bs, Body, Bucks, Boxes, Bytes, and Brains. When we talk about last mile it is the most volatile part of the chain and requires special consideration like routing and locating warehouses with barrier consideration which I did my thesis on.
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The SARS-CoV-2 (Covid-19) coronavirus pandemic has, in some respects, through interrupted chains of international supply and supply logistics, reduced the scale of economic globalisation processes. On the other hand, the need for the development of remote Internet communication has increased due to the introduction of home quarantine periods and lockdowns imposed on selected sectors of the economy in 2020. Consequently, the scale of internetisation and digitalisation of various aspects of business conducted by companies and enterprises has increased. As a result of the growth of the Internet, the importance of information globalisation has increased in terms of remote Internet-based communication. In a multi-year perspective, the importance of environmental and pro-climate globalisation may increase in the future. In view of the above, how else will globalisation processes change in this decade of the 21st century? Will the current energy crisis, the unfolding food crisis, the migration crisis
What is your opinion on this?
What is your opinion on this topic?
Please reply,
I invite you all to discuss,
Thank you very much,
Best regards,
Dariusz Prokopowicz
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Globalization is today, without a doubt, the most discussed and studied topic, attacked and defended throughout the world and it would seem to be the center of controversy.
There are conceptions that maintain that globalization, as it is known and implemented today, configures the decline of the sovereignty of the state against other states and/or international and supranational organizations.
Many times personified in what is called the great village, its maximum symbol, we could say that today the internet is a useful instrument as a tool, but at the same time, destroyer of cultures and regionalisms as rich and at the same time unexplored as those that They exist all over the world, from north to south and from east to west.
Many times cataloged as an agent of cultural infiltration, globalization sympathetically hides behind what some scholars call the new age culture and the light man.
As a model, globalization has the capacity to destroy the internal regional economies of a country and provoke a regional imbalance in terms of economic, political and productive aspects, and in the name of a regional integration that is not such, to lead the population (which is in whom, in theory, the positive effects of this model should be noticed), to a human condition that is often humiliating.
We see how the countries must all adopt an economic model that seems to be the indispensable tool of this globalization, neo-liberalism whose main objective is to shrink the state so much that it will no longer be able to stop, not even minimize or counteract the negative effects of the globalization.
We appreciate the unsustainable appearance of a social apathy where private interest prevails and interest in others disappears, killing a fundamental instrument that is the one that makes it possible for a society to be such and that allows each person to contribute to the general interest in for the common good, and that results in benefits for the whole society.
We are without a doubt facing a phenomenon, which if it is not legislated, studied and used correctly, we could call it: THE MONSTER OF THE 21ST CENTURY
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Have any survey about a logistics differentiation in a supply chain context?
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I am trying to identify the three strongest environments in the world for simulation and system dynamics in Operations, logistics and/or Supply chain management - can anyone suggest some environments?
With strong, I mean that have research groups, graduate trainings on the method and UTD-list scientific publications that applies it.
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I going to suggest 2 resources for you. The first is. One of Goldratt's disciples. The second is the management science group at Michigan State University. Both have done excellent work in this area.. The screenshot shows Dr Choe's location. Best wishes David Booth
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What do you think are the key factors that could trigger a food crisis in your country in the next quarters and/or years?
Due to many different factors, a food crisis can develop in many countries. The international supply and supply logistics chains that were interrupted during the SARS-CoV-2 (Covid-19) coronavirus pandemic have not been fully rebuilt. Rising fuel prices are driving up the cost of transporting food products to shops. The decline in fertiliser production is also driving up the cost of producing crops. In addition, the war in Ukraine has resulted in a decline in cereal supplies to many countries. The lack of electricity has caused a decline in the production of nitrogenous fertilisers. This then caused a decrease in the production of CO2, which benefits producers of many types of food products. Many food product factories are raising the prices of their products due to increases in raw material, energy and fuel prices. Many production facilities are reducing the scale of production. There may be job cuts. Consumption is falling due to high inflation. If a downturn in the economy occurs in the next quarters, many companies may go out of business and unemployment will rise. In addition, periods of increasingly severe drought, more and more hot days and less and less rain and more and more frequent fires in many parts of the world are causing a significant drop in crop production in agriculture. On the other hand, further food crises may arise in the future in the long term, which will be the result of a global climate crisis developing on a multi-year scale.
In view of the above, I address the following question to the esteemed community of researchers and scientists:
What do you think are the key factors that could trigger a food crisis in your country in the next quarters and/or years?
What should be done to reduce the scale of development of the food crisis?
What is your opinion on the subject?
What do you think about this topic?
Please reply,
I invite you all to discuss,
Thank you very much,
Regards,
Dariusz Prokopowicz
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Dear Dariusz
I dare to assert that all epigenetic factors may initiate a food crisis (Social interaction, war, disease, financial, created deficiencies etc.) More important is to have strategies to contain an emerging problem so that it will not spread globally. To avoid that we formed the UNO. In the post war era we also created the global market. Unfortunately, this is an economic concept that is based on competition and the winner in competition are those with with power. Now we are witnessing the global economy is dividing and consolidating in blocks. Unfortunately, the blocksformation is permananentl restructured and driven by a few war mongers. For them power is more important than food for the global citizen.
Many people are afraid and believe that we have too many people on the globe. For them war is the preferred mode to assure access to food. They cant imagine that with innovation and new circulatory technologies it is possible to multiply food supply. Food shortage is always a distribution problem. There are many actors around that increase value through shortage. The alternative is entrepreneurial collaboration with advanced technologies to assure a very large diversity of healthy food, enhanced with free global trade via internet. Science has failed to convince taxpayer that with sustainable technologies we will always have enough food for everybody.
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I am looking for journals with no APC in the field of transport and logistics.
Please give some recommendations
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I have found that a journal called "Transportation Reviews" state on their website no submission or publication fees. I have also found a journal called "Transportation" from the publisher recommended by Nolberto Munier - charges are not mentioned specifically on the website but seems to be implied that there are no charges - suggest you email them if you want to confirm with certainty. Another one, also clearly stated no submission or publication fees; International Journal of Logistics Research and Management.
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Can anyone kindly share the details of the procedure or software or link of it for analyzing critical crop weed competition from the two sets of weedy and weed-free situations in a cropped field
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I don't know if you still need an answer, but you might try this software:
If you could share some real data that I can use for teaching purposes, I can do it for you.
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2 Logistic chaotic sequences generation, we are generating two y sequence(Y1,Y2) to encrypt a data
2D logistic chaotic sequence, we are generating x and y sequence to encrypt a data
whether the above statement is correct, kindly help in this and kindly share the relevant paper if possible
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after reading an article baesd on quantum image encryption I think these two chaotic sequences are used for a key generation, not for encryption.
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I have two curves, the first one is a growth curve of a microorganisms reported as cells/mL, whereas the second one is the concentration (as % of the initial concentration) of a specific compound.
I'm trying to understand if there is any correlation between the 2 curves (hence I want to know if the reduction of the compound is depended on the microorganisms growth) but I'm not sure which solution is the best. I tried Pearson correlation but I'm not sure it's the best method since the curves are not linear but they have both a logistic type curve.
I've attached an excel file where I reported an example of the data.
Thanks for your help
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1st : Please see the Excel attachment for 7 steps for doing the cubic plot.
2nd : About t- and F- distribution statistics and p-values that come from Excel Regression Analysis (which you "... couldn't find the functions to obtain those data on my excel ..."):
google-search for
"Why is Data Analysis not showing in Excel?"
... as soon as you add-it-in ... "the Data Analysis ToolPak"
then look for / google
"excel data analysis toolpak regression"
Good Luck
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Will drones evolve as a parcel delivery model or as objects of intelligent logistics networks meaning that they will deliver to distribution centers not to final users?
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May be helpful for your work: